Senior Systems Software Engineer, Accelerated Kubernetes and Scale - DGX Cloud

Senior Systems Software Engineer – DGX Cloud, NVIDIA

NVIDIA is a leader in hardware and software innovation for AI, driving advancements across gaming, accelerated computing and autonomous systems. The DGX Cloud organization delivers industry‑leading infrastructure for the world’s most ambitious AI workloads.

• Lead end‑to‑end performance and scalability analysis across the Kubernetes‑based accelerated runtime stack (control and data planes), including NVIDIA components such as GPU Operator, Network Operator, node‑feature‑discovery, topograph, dra‑driver‑nvidia‑gpu, and nvsentinel, tracking issues from orchestration down to the metal.
• Design and contribute upstream architectural changes to the Kubernetes control plane and related projects to enable reliable operation at hyperscale cluster sizes.
• Improve container startup and cold‑start latency to enable smooth, low‑latency inference scaling on Kubernetes across thousands of GPU nodes, ensuring the AI runtime stack scales without creating API server pressure or operational fragility.
• Assess, improve, and contribute to open‑source projects that make Kubernetes an outstanding platform for AI workloads (e.g., Grove and gateway‑api‑inference‑extension), focusing on scalability, resilience, and multi‑node training/inference.
• Advance scalability and performance of confidential containers (CoCo) on Kubernetes so encrypted inference workloads meet stringent efficiency and latency requirements in production.
• Use DSX and related large‑scale simulation infrastructure to model full AI‑factory deployments and validate scalability across thousands of simulated GPUs, catching failures that emerge only at scale before hardware arrives.
• Collaborate with AI researchers, developers, customers, and upstream communities to design automated, at‑scale workload tests (including replay of production agent traces), build monitoring/analysis tooling, and integrate continuous performance and scale testing into modern CI/CD workflows.
• Document methods and results clearly and present findings internally and at industry events (for example, Kube Con, GTC), while actively engaging with upstream groups (Kubernetes SIG Scalability, CNCF, and NVIDIA OSS communities) to influence and validate AI workload performance and scalability directions.

• Bachelor’s or Master’s degree in Engineering or equivalent experience, ideally in Electrical, Computer Engineering, or Computer Science.
• 8+ years of experience in computer architecture, networking, storage systems, and accelerator‑based platforms.
• Expertise in Kubernetes and familiarity with the broader CNCF ecosystem.
• Deep experience with large‑scale, parallel, distributed accelerator systems and performance optimization of AI workloads.
• Experience with performance modeling and benchmarking for large‑scale systems.
• Proficiency in Golang and/or Python.
• Strong familiarity with the NVIDIA software stack across training and inference.
• Expertise with at least one major public cloud provider (e.g., AWS, Azure, GCP, or OCI).

• Strong operational experience with any one of the Kubernetes distributions.
• Prior experience scaling Kubernetes clusters to ultra‑large node and object counts.
• Demonstrated history of working in the open‑source community.
• Excellent communication and interpersonal abilities.
• PhD or equivalent experience in relevant areas.

Base salary is determined based on location, experience, and market comparables. For Level 4, the range is 184,000

USD – 287,500

USD; for Level 5, the range is 224,000

USD – 356,500

USD. You will also be eligible for equity and benefits.

NVIDIA is an equal opportunity employer and is committed to fostering an inclusive work environment. We do not discriminate on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.